Search Results for author:
Found 10 papers, 8 papers with code
TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision
Among these works, many of them utilize in-context examples to achieve generalization without the need for fine-tuning, while few of them have considered the problem of how to select and effectively utilize these examples.
Entropy-Regularized Token-Level Policy Optimization for Large Language Models
At the heart of ETPO is our novel per-token soft Bellman update, designed to harmonize the RL process with the principles of language modeling.
Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training
Empirical results across reasoning, planning, alignment, and decision-making tasks show that TS-LLM outperforms existing approaches and can handle trees with a depth of 64.
Large Sequence Models for Sequential Decision-Making: A Survey
Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e. g., GPT-3 and Swin Transformer.
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
In this paper, we introduce a novel architecture named Multi-Agent Transformer (MAT) that effectively casts cooperative multi-agent reinforcement learning (MARL) into SM problems wherein the task is to map agents' observation sequence to agents' optimal action sequence.
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks
In this paper, we facilitate the research by providing large-scale datasets, and use them to examine the usage of the Decision Transformer in the context of MARL.
Offline Pre-trained Multi-Agent Decision Transformer
Offline reinforcement learning leverages static datasets to learn optimal policies with no necessity to access the environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
In this paper, we extend the theory of trust region learning to MARL.
Settling the Variance of Multi-Agent Policy Gradients
In multi-agent RL (MARL), although the PG theorem can be naturally extended, the effectiveness of multi-agent PG (MAPG) methods degrades as the variance of gradient estimates increases rapidly with the number of agents.
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Our framework is comprised of three key components: (1) a centralized task dispatching model, which supports the self-generated tasks and scalable training with heterogeneous policy combinations; (2) a programming architecture named Actor-Evaluator-Learner, which achieves high parallelism for both training and sampling, and meets the evaluation requirement of auto-curriculum learning; (3) a higher-level abstraction of MARL training paradigms, which enables efficient code reuse and flexible deployments on different distributed computing paradigms.
